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Proceedings Paper

Medical image segmentation by combing the local, global enhancement, and active contour model
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Paper Abstract

The objects in the medical images are not visible due to low contrast and the noise. In general, X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) images are often affected by blurriness, lack of contrast, which are very important for the accuracy of medical diagnosis. It is difficult to segmentation in such case without losing the details of the objects. The goal of image enhancement is to improve certain details of an image and to improve its visual quality. So, image enhancement technology is one of the key procedures in image segmentation for medical imaging. This article presents a two-stage approach, combining novel and traditional algorithms, for the enhancement and segmentation of images of bones obtained from CT. The first stage is a new combined local and global transform domain-based image enhancement algorithm. The basic idea of using local alfa-rooting method is to apply it to different disjoint blocks of different sizes. We used image enhancement non-reference quality measure for optimization alfa-rooting parameters. The second stage applies the modified active contour method based on an anisotropic gradient. The simulation results of the proposed algorithm are compared with other state-of-the-art segmentation methods, and its superiority in the presence of noise and blurred edges on the database of CT images is illustrated.

Paper Details

Date Published: 24 June 2019
PDF: 7 pages
Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990Q (24 June 2019); doi: 10.1117/12.2519584
Show Author Affiliations
V. Voronin, Don State Technical Univ. (Russian Federation)
Moscow State Univ. of Technology "STANKIN" (Russian Federation)
Evgenii Semenishchev, Moscow State Univ. of Technology "Stankin" (Russian Federation)
Marina Pismenskova, Moscow State Univ. of Technology "Stankin" (Russian Federation)
O. Balabaeva, Don State Technical Univ. (Russian Federation)
S. Agaian, The City Univ. of New York, College of Staten Island (United States)


Published in SPIE Proceedings Vol. 10999:
Anomaly Detection and Imaging with X-Rays (ADIX) IV
Amit Ashok; Joel A. Greenberg; Michael E. Gehm, Editor(s)

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